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187 results for “digital imaging”
FIGURE 1 in A novel application of the white light/fringe projection duo: recovering high precision three-dimensional images from fossils for the digital preservation of morphology
FIGURE 1. Optical set up specifications for fringe projection profilometry (FPP) used in this study for the recovery of a 3-D image of a hemimandible sample.
FIGURE 7 in A novel application of the white light/fringe projection duo: recovering high precision three-dimensional images from fossils for the digital preservation of morphology
FIGURE 7. Examples of analyses that can be performed with the obtained data from the fossils: 1) denoting the relief (emboss filter), 2) detecting edges and transitions (sobel filter), 3) study of the roughness and waviness of a sample (topography filter).
FIGURE 2 in A novel application of the white light/fringe projection duo: recovering high precision three-dimensional images from fossils for the digital preservation of morphology
FIGURE 2. Flowchart of the 'OTY' procedure employed in this study, where: α = angle (60º in this case), * = MBE algorithm (Gutiérrez-García et al., 2013), and ** = Goldstein algorithm. OTY: name given to the white light system together with the phase shifting algorithm filter, based on the fact that it was developed for use on Ototylomys samples (see main text).
FIGURE 6. Full 3-D in A novel application of the white light/fringe projection duo: recovering high precision three-dimensional images from fossils for the digital preservation of morphology
FIGURE 6. Full 3-D image of the reconstructed fossil after merging all the six views. 1) Cloud of points, 2) Final mesh.
FIGURE 4 in A novel application of the white light/fringe projection duo: recovering high precision three-dimensional images from fossils for the digital preservation of morphology
FIGURE 4. Process applied to recover the topography of the fossil sample. Where: 1) image captured by the CCD of the fringe projection on the sample, 2) wrapped phase obtained of the 8 frames after applying the MBE filter, 3) unwrapped phase map, 4) phase carrier compensation, and 5) surface of one of the views of the fossil recovered.
Digital Elevation Models from Planetary Flyby Images of Mercury and the Moon with Shape and Albedo from Shading
<p>Supplemantary material to Krüll, I., Wohlfarth, K., Tenthoff, M., Wöhler, C., Galluzzi, V., Wright, J., Benkhoff, J., and Zender, J.: Shape and Albedo from Shading with Planetary Flyby Images of Mercury and the Moon, Europlanet Science Congress 2024, Berlin, Germany, 8–13 Sep 2024, EPSC2024-247, https://doi.org/10.5194/epsc2024-247, 2024.</p> <p><strong>Abstract</strong></p> <p>Surface reconstruction of planetary bodies such as the Moon and Mercury is crucial for geomorphological analysis, reflectance normalization, thermal modeling, rover landing site planning, and outreach activities. Stereo algorithms and Shape-and-Albedo-from-Shading (SAfS) are well-established methods for planetary 3D reconstruction. SAfS refines the surface slopes of a stereo Digital Elevation Model (DEM) and typically yields 3D models at image resolution. This approach is well-validated for scientifically calibrated instruments that observe the planetary body under favorable conditions. This work applied the SAfS algorithm to more challenging planetary flyby images acquired with uncalibrated off-the-shelf cameras. We investigated three scenarios: a fly-by image of the Moon captured by a GoPro during the Artemis I mission, a fly-by image of Mercury which was obtained with a monitoring camera during BepiColombo’s third flyby, and a telescope image taken in Wetter, Germany. We qualitatively and quantitatively assessed the algorithm's performance. The results of the two flyby images indicate that, despite the challenging conditions, the SAfS algorithm could reconstruct the surface up to image resolution and increase the level of detail of the input DEM. The reconstructed DEM of the telescope image is the one with the lowest resolution. All in all, our flyby-derived DEMs are accurate. They provide excellent outreach products, as demonstrated by ESA's BepiColombo flyby movie: https://www.esa.int/Science_Exploration/Space_Science/BepiColombo/BepiColombo_s_third_Mercury_flyby_the_movie</p> <p><strong>Dataset<br></strong></p> <p>We applied the SAfS algorithm to different Regions of Interest (ROIs) in the flyby and telescope images. The ROIs are marked in Artemis_Flyby_ROIs.png, Bepicolombo_Flyby3_ROIs.png and Moon_Telescope_ROIs.png, respectively. For each ROI a DEM is provided centered on the latitude and longitude (0-360, positive east) in the filename. Furthermore a Red/ Blue Stereo anaglyph of the original image was created with the SAfS DEM (for this purpose the height has been exaggerated).</p> <p> </p>
Dataset for the publication "Identification of plasticity-induced crack closure by using high-resolution digital image correlation"
<p>This repository publishes the data generated in the article "Identification of plasticity-induced crack closure by using high-resolution digital image correlation" (see arxiv preprint <a href="https://arxiv.org/html/2409.02560v1">Plasticity-induced crack closure identification during fatigue crack growth in AA2024-T3 by using high-resolution digital image correlation (arxiv.org)</a>)</p> <p>This repository is structured with the following subfolders:</p> <ul> <li><strong>0_fe_data: </strong>contains the displacement field of the free surface of the 3D finite element model that were used to determine the crack opening curves and, in the following, the crack opening value Kop</li> <li><strong>1_hrdic_data: </strong>contains the high-resolution DIC displacement field data at a crack length of 27.8 mm at different load levels, starting from minimum load 1.5 kN to maximum load 15 kN</li> </ul>
CHARACTERIZATION OF BREAST LESIONS BY PROCESSING DIGITAL BREAST IMAGES
<p><span>This Rendering to the World Health Organization, women in both developed and developing nations are most likely to develop breast cancer. This illness causes breast cells to grow and multiply out of control. According to research institutes and international organizations, there are various screening methods available based on age, and breast cancer can be cured if detected in time. The Breast Imaging Reporting and Data System (BIRADS) is a standardized system that is commonly used in these techniques to report results and findings. Results are sorted by BIRADS into six categories, numbered 0 through 6. Furthermore, mammography is the most widely utilized screening technique.</span></p> <p><span>This study suggests using mammography data processing to identify breast lesions. Adaptive filters are used for image cropping and contrast enhancement during the pre-processing phase. The pectoral muscle is then segmented using segmentation techniques that consider morphological and area growth factors. The lesion is then divided into sections at the muscle and breast levels using the Discrete Wavelet Transform (DWT), which finds any micro calcifications. Furthermore, to distinguish between dense lesions and other kinds of lesions, an area cultivation approach combined with multiple thresholding techniques is employed. Lastly, the obtained segmentation is used to extract textural and morphological features.</span></p> <p><span>When expert-segmented and automatically segmented images were compared, the Sorensen Decade similarity index was 0.73, indicating the effectiveness of the suggested method. Considering that the lesion area on a mammogram can only be roughly delineated by hand or automatically, this is a promising outcome.</span></p>
Dataset Literature Review Digital Forensic and Image Processing
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "<em>digital forensic</em>" dan "<em>image processing</em>"</p>
Dataset Literature Review Digital Forensic AND Image Processing
<p>Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "digital forensic" dan "image processing" </p>
Digital elevation models, ortho images and outlines of Yala Glacier, Langtang Valley, Nepal Himalaya
<p>Datasets related to article "Up-glacier propagation of surface lowering of Yala Glacier, Langtang Valley, Nepal Himalaya". The data includes three digital elevation models (DEM), two ortho images and five outlines of Yala Glacier between 1981 and 2015.<br> </p> <p>Description of files:<br> 1) DEM and ortho image</p> <p>- 1981Yala_dem_20_-10_bias_cor.tif: 10 m resolution digital elevation model derived from a map that was generated using ground phogogrammetry images that were acquired in 1981 (Yokoyama, 1984; Fujita and Nuimura).</p> <p>- 2007Yala_dem_0_-4_bias_cor.tif: 2 m resolution digital elevation model derived from 14 oblique photographs that were acquired by a private jet with handheld cameras in 2007.<br> - 2007Yala_ortho.tif: an ortho images derived by the same data in 2007.</p> <p>- 2015Yala_dem_0_0_bias_cor.tif: 1 m resolution digital elevation model derived from 519 photographs that were acquired by a UAV-based photogrammetric survey in 2015.<br> - 2015Yala_ortho.tif: an ortho images derived by the same data in 2015.<br> <br> 2) Glacier boundary (shapefile Files)</p> <p>- Yala_area_1981: <br> - Yala_area_2007:<br> - Yala_area_2009:<br> - Yala_area_2012:<br> - Yala_area_2015:<br> <br> <br> Please see the related journal article for details on datasets.<br> <br> Sunako, S., Fujita, K., Izumi, T., Yamaguchi, S., Sakai, A., & Kayastha, R. (2023). Up-glacier propagation of surface lowering of Yala Glacier, Langtang Valley, Nepal Himalaya. Journal of Glaciology, 69(274), 425-432. doi:10.1017/jog.2022.118<br> </p>
Image Dataset for 'Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning'
<p>Dataset used in the manuscript 'Digitally Deconstructing Leaves in 3D Using X-ray microcomputed Tomography and Machine Learning'. Please cite the paper presenting this dataset:</p> <p><strong>Citation:</strong> Théroux-Rancourt, G., M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, and J. M. Earles. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography<strong> </strong>and machine learning. <em>Applications in Plant Sciences</em> 8(7): .</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>A 'Cabernet Sauvignon' grapevine (<em>Vitis vinifera</em> L.) leaf from a plant of the BOKU experimental vineyard in Tulln, Austria, was scanned using microCT at the Swiss Light Source. The original reconstructions of the scans are using the gridrec (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Gridrec_reconstruction_downsized.zip?versionId=28d98982-f69d-4eac-9dfa-efcc89c6823c">Gridrec_reconstruction_downsized.zip</a>) and the paganin, or phase-contrast, algortithm (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Phase_contrast_reconstruction_downsized.zip?versionId=bef3260d-2865-4c9b-b5e1-e692edefb691">Phase_contrast_reconstruction_downsized.zip</a>). To facilitate automated segmentation, the size of the image in the <em>x </em>and <em>y</em> dimensions have been halved, so that the size of the pixels is 0.325 µm in those dimensions, but 0.1625 µm in the <em>z</em> (slices) dimension.</p> <p>A binary image segmenting the leaf cells and the airspace for each gridrec and phase-contrast stacks are created, and both are combined together (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Binary_stack_for_local_thickness.zip?versionId=165e3938-b490-4e56-9c8e-a2084cb39d49">Binary_stack_for_local_thickness.zip</a>), a map of the local thickness is created (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Local_thickness_map.zip?versionId=ce0a7dc7-5e3f-44a4-8881-cf84b6efd87c">Local_thickness_map.zip</a>). This map gives information on the largest diameter of the pixels labeled as cells in the binary stack.</p> <p>Hand-labeled slices or ground truths were drawn on the following slices: 80, 140, 200, 260, 340, 400, 440, 540, 620, 740, 800, 860, 940, 1060, 1140, 1240, 1300, 1400, 1480, 1540, 1600, 1690, 1740, 1840 (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Hand_labelled_slices.tif?versionId=a21a13ac-fa47-4ef8-a903-ecc433787184">Hand_labelled_slices.tif</a>).</p> <p>Using the hand-labeled slices and the different images, a random-forest model was trained, which allowed to automatically segment the remaining slices of the stack (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Fullstack_Prediction_Example-6_training_slices-6_testing_slices.zip?versionId=02b69e65-da85-492e-9b72-9b2b3ccd085f">Fullstack_Prediction_Example-6_training_slices-6...</a>).</p> <p>The source code for the segmentation program is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/master">here</a>, and the source code for the testing used in the paper is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/nb-slices-eval">here</a>.</p>
Historical German Children's Playbooks - 6 Digitized Books with Images, OCR-Fulltext, and Named Entity Recognition
<p>The dataset consists of 6 digitized books with 1750 images and OCR-fulltext.</p> <p>Additionally, named entity recognition has been carried out on basis of flair's de-ner model, see https://github.com/flairNLP for details.</p>
Digital Image Correlation Workshop at the University of Manchester, September 2016
<p>Enabling Process Technologies sponsored a Digital Image Correlation (DIC) workshop on 8 September, 2016 at the School of Mechanical, Aerospace and Civil Engineering at the University of Manchester. Alistair Tofts, Director of Sales & Marketing at Correlated Solutions attended and provided a series of presentations covering DIC theory, and the working principles of the VIC lineup of DIC software.</p> <p>Afterwards, three discrete tests were conducted with the same specimen and speckle on three different frames and setups:</p> <ul> <li>High speed: Zwick HTM-5030 test frame, Photron SA.1 high speed cameras</li> <li>Quasi-static 2D: Instron 5969 test frame, Point Grey Research machine vision camera</li> <li>Quasi-static 3D: Instron 5659 test frame, 2 Point Grey Research machine vision cameras</li> </ul> <p>The archives included in this dataset are:</p> <ul> <li>Presentations: contains the slides from the presentations given by Alistair Tofts, as well as an outline/agenda given by Matthew Roy.</li> <li>3D_fast, 2D_slow and 3D_slow: correspond to the tests/setup described above, including all calibration and test images, along with VIC metadata for replaying analyses.</li> </ul> <p>Specimen geometry is shown on slide 6 of ~/Presentation/DICWorkshopPresSept2016_MJR.pptx, and markdown readme files are included to describe the test and data.</p>
Residual strains estimations in the annulus fibrosus through digital image correlation
<p>This upload correspond to the experimental data and all the python and LMGC software pipeline to process the data.</p> <p>The raw data correspond to images of the stress/strain relaxation process within the annulus fibrosus following a radial cut.</p> <p>The corresponding scientific article has been published in the following Diamond Open Access Journal : Journal of Theoretical, Computational and Applied Mechanics.</p>
Full-field displacements and strains obtained by digital image correlation during fatigue crack growth experiments
<p>This data publication contains full-field displacements and strains obtained by 3D digital image correlation (DIC) using a GOM Aramis 12M system including three fatigue crack propagation (fcp) experiments of AA2024-T3 aluminium sheet material.</p> <p>The repository consists of three datasets of different experiments named </p> <ul> <li>S<sub>950,1.6 </sub>- MT950 specimen, 1.6 mm sheet thickness, load ratios R=0.3, 1.0</li> <li>S<sub>160,2.0 </sub>- MT160 specimen, 2.0 mm sheet thickness, load ratios R=0.1, 0.25, 0.5, 0.75, 1.0</li> <li>S<sub>160,4.7 </sub>- MT160 specimen, 4.7 mm sheet thickness, load ratios R=0.1, 0.25, 0.5, 0.75, 1.0</li> </ul> <p>where S<sub>w,t</sub> denotes a middle tension (MT) specimen with width w and thickness t. The nodal DIC measurements at different times during the experiments are provided as .txt files we call <em>"nodemaps" </em>and stored in subfolders "<strong>Nodemaps</strong>". Each <em>nodemap</em> consists of a header containing meta data information like a running number (current stage index) or the applied force, followed by the nodal displacements and strains in tabular form. Additionally, the dataset S<sub>160,4.7</sub> contains crack path and crack tip labels for each nodemap in the subfolder "<strong>GroundTruth</strong>". The ground truth is provided as arrays of size 256x256. Each pixel of the array contains the label "2" for the class "crack tip", "1" for the class "crack path", or "0" for the class "background". These labels were created in a semi-manual fashion and can be used for machine learned crack detection using supervised training.</p> <p>These datasets were recently used to evaluate neural attention of convolutional neural networks trained on fatigue crack tip detection in <a href="https://www.nature.com/articles/s41598-022-13275-1">Melching et al. (Sci Rep, 2022)</a>. Additional guidance on data loading and usage can also be found at <a href="https://github.com/dlr-wf/explainable-crack-tip-detection">https://github.com/dlr-wf/explainable-crack-tip-detection</a>.</p> <p>The experiments S<sub>160,2.0</sub> and S<sub>160,4.7 </sub>were conducted and analysed by <a href="https://doi.org/10.1111/ffe.13433">Strohmann et al. (FFEMS, 2021)</a>.</p> <p>The experiment S<sub>950,1.6</sub> was conducted and analysed by <a href="https://doi.org/10.1111/ffe.13335">Breitbarth et al. (FFEMS, 2020)</a>.</p>
Coleopsis archaica digital microscopy images
<p>Images taken on a Keyence VHX 7000 digital microscope</p>
A method to evaluate body length of live aquatic vertebrates using digital images
<p>Traditional methods to measure body lengths of aquatic vertebrates rely on anesthetics, and extended handling times. These procedures can increase stress, potentially affecting the animal's welfare after its release. We developed a simple procedure using digital images to estimate body lengths of coastal cutthroat trout (<i>Oncorhynchus clarkii clarkii</i>) and larval coastal giant salamander (<i>Dicamptodon tenebrosus</i>). Images were post-processed using ImageJ2. We measured more than 1,800 individuals of these two species from 200 pool habitats along 9.6 river kilometers. The percent error (mean ± SE) of our approach compared to the use of a traditional graded measuring board was relatively small for all metrics of the two species. Total length of trout was -2.2% ± 1.0. Snout-vent length and total length of larval salamanders was 3.5% ± 3.3 and -0.6% ± 1.7, respectively. We cross-validated our results by two independent observers that followed our protocol to measure the same animals and found no significant differences (<i>p</i> > 0.7) in body size distributions for all metrics of the two species. Our procedure provides reliable information of body size reducing stress and handling time in the field. The method is transferable across taxa and the inclusion of multiple animals per image increases sampling efficiency with stored images that can be reviewed multiple times. This practical tool can improve data collection of animal size over large sampling efforts and broad spatiotemporal contexts.</p>
An Image-Based Gamut Analysis of Translucent Digital Ceramic Prints for Coloured Photovoltaic Modules: Supplementary Data
<p>Colouring the frontglass of PV modules via digital ceramic printing aids in concealing the PV when integrated into existing building façades as BIPV, while admitting sufficient light to produce electricity. This promotes the visual acceptance and adoption of PV as a source of renewable energy in urban environments. The effective colour of the PV laminate is a combination of the transparent colour on glass and the colour of the PV cells. This colour should ideally match the architect’s visual expectations in terms of fidelity, but also in terms of relative PV efficiency as a function of print density. In practice, these requirements are often contradictory, particularly for vivid colours, and the visual results may deviate significantly. This paper presents an objective analysis of how colours appear on PV frontglass laminated with a PV module, using an image-based colour acquisition process. Given a set of 1044 nominal colours uniformly distributed in the RGB colour space, each printed in 10 opacities, we quantify the range of effective colours observed when printed on glass and combined with PV, and their deviation from the nominals. Our results confirm that the effective colour gamuts are significantly constrainted and skewed, depending on the ink volume and glass finish used for printing. In particular, blue-magenta hues cannot be reliably rendered with this process. These insights can serve as guidelines for selecting target colours for BIPV that can be well approximated in practice.</p>
MACADAMIA Sloan Digital Sky Survey Asteroid Photometry, Measurements, and Images
<p>This dataset contains data from the Multi-Archive Catalog of Asteroid Detections And Measurements for Interactive Access (MACADAMIA), specifically of numbered asteroids observed serendipitously by the Sloan Digital Sky Survey (SDSS).</p> <p>photometry_archive_20201027.db is a SQLite database containing data corresponding to a search of the SDSS image archive conducted on 2020 May 1 of all numbered asteroids known at the time, which yielded 2.84 million search results, 1.98 million detections that were determined to actually be within the field of view of the identified image, and 993,777 successfully measured detections. Preview images of all detections and non-detections where the object was determined to be within the field of view of an identified image are collected in the detection_previews_*tar.gz files in this dataset, indexed by detection_id in the photometry database and listed under preview_image_file in the detection_data table.</p> <p>photometry_archive_20210927.db contains an update to the SDSS search, containing data corresponding to a search of the SDSS image archive conducted on 2021 July 2 of all numbered asteroids known at the time, which yielded 2.96 million search results, 2.06 million detections that were determined to actually be within the field of view of the identified image, and 1.02 million successfully measured detections. Previews of additional detections identified in this updated search may not be available due to data loss in a system failure in August 2023.</p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.